Utility-Managed EV Charging & Grid Load Management
Primary service
Real-time Monitoring & Anomaly Detection
Secondary service
Data Modeling & Lineage
Proposed Cadvel capability — not a client case study.
Target client and segment
Distribution utilities and residential charging program operators
Challenge
Utilities need to coordinate charging with local network limits while keeping drivers ready to leave. That requires a trusted view of charging demand, connection points and customer preferences.
Key capabilities
- Visibility of charging load by site and mapped network connection.
- Monitoring of charging schedules against agreed capacity and departure needs.
- An exception workflow for missed schedules, stale data and charger faults.
Business Challenge
Charging load lacks network context
Charger identifiers do not automatically map to service points, transformers or feeders. Network-level aggregation depends on validated connection data.
Customer needs compete with load limits
Departure times, minimum charge requirements and overrides must be available to the scheduling service and visible in performance reports.
Stale measurements hide breaches
A quiet charger may be offline rather than idle. Dashboards must show freshness and coverage alongside apparent load.
Commissioning errors persist
Incorrect site mappings or power ratings can undermine load calculations long after an installer leaves the property.
Architecture and Tech Stack
Charging management APIs and site meters → event validation → versioned charger–service point–network model → load and schedule analytics → utility dashboard and exception queue. Supply validated constraints to the client’s charging scheduler.
Technology options
Kafka for charger events where volume warrants streaming; ClickHouse for load histories; Datadog for pipeline and API health. AWS IoT Core is an optional device-ingestion route when the hardware supports it; existing platform APIs may be sufficient.
Delivery boundary
Network limits come from the utility. The charging controller enforces them locally or through an approved control service; an analytical dashboard is not a protection system.
Implementation and Solution
Proposed implementation stages
Map the charging program
Define service points, network limits, driver commitments and the initial charger cohort.
Validate load data
Connect session and meter feeds; check units, timestamps, ratings and commissioning mappings.
Compare schedules with delivery
Join planned charging with actual load and driver overrides; test stale-data and fault alerts.
Pilot and extend
Validate a limited network area with operators, then expand by hardware type and connection group.
What this enables
Measurable success criteria — to be agreed and validated with the client; not achieved results.
Constraint breaches
Minutes above the approved site or network limit per reporting period, using valid meter data.
Departure readiness
Eligible sessions meeting the agreed energy requirement by departure ÷ eligible sessions.
Peak charging demand
Maximum interval-average EV kW per connection group, compared over equivalent observation windows.
Fault detection latency
p95 time from a confirmed charger fault to an actionable operator alert.
KPI targets and acceptance tolerances are set with the client during discovery; no achieved outcomes or ROI are asserted.
Source inspiration / reference
Source-derived facts
Codibly’s Banyon Power example covers commissioning, homeowner schedules, charging sessions and utility administration. It describes fault tracking, role-based access and AWS Cloud with IoT Core. The network mapping and KPI design here are proposed Cadvel scope, not reported Banyon outcomes.
Reference context only. Proposed Cadvel design and KPIs are independent of the source delivery.
